| 1. |
- Assi, Nada, et al.
(författare)
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Are Metabolic Signatures Mediating the Relationship between Lifestyle Factors and Hepatocellular Carcinoma Risk? Results from a Nested Case–Control Study in EPIC
- 2018
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - 1055-9965 .- 1538-7755. ; 27:5, s. 531-540
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Tidskriftsartikel (refereegranskat)abstract
- Background: The "meeting-in-the-middle" (MITM) is a principle to identify exposure biomarkers that are also predictors of disease. The MITM statistical framework was applied in a nested case-control study of hepatocellular carcinoma (HCC) within European Prospective Investigation into Cancer and Nutrition (EPIC), where healthy lifestyle index (HLI) variables were related to targeted serum metabolites.Methods: Lifestyle and targeted metabolomic data were available from 147 incident HCC cases and 147 matched controls. Partial least squares analysis related 7 lifestyle variables from a modified HLI to a set of 132 serum-measured metabolites and a liver function score. Mediation analysis evaluated whether metabolic profiles mediated the relationship between each lifestyle exposure and HCC risk.Results: Exposure-related metabolic signatures were identified. Particularly, the body mass index (BMI)-associated metabolic component was positively related to glutamic acid, tyrosine, PC aaC38:3, and liver function score and negatively to lysoPC aC17:0 and aC18:2. The lifetime alcohol-specific signature had negative loadings on sphingomyelins (SM C16:1, C18:1, SM(OH) C14:1, C16:1 and C22:2). Both exposures were associated with increased HCC with total effects (TE) = 1.23 (95% confidence interval = 0.93-1.62) and 1.40 (1.14-1.72), respectively, for BMI and alcohol consumption. Both metabolic signatures mediated the association between BMI and lifetime alcohol consumption and HCC with natural indirect effects, respectively, equal to 1.56 (1.24-1.96) and 1.09 (1.03-1.15), accounting for a proportion mediated of 100% and 24%.Conclusions: In a refined MITM framework, relevant metabolic signatures were identified as mediators in the relationship between lifestyle exposures and HCC risk.Impact: The understanding of the biological basis for the relationship between modifiable exposures and cancer would pave avenues for clinical and public health interventions on metabolic mediators.
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| 2. |
- Agogo, George O., et al.
(författare)
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A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
- 2016
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. Methods: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. Results: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. Conclusions: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.
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| 3. |
- Fages, Anne, et al.
(författare)
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Investigating sources of variability in metabolomic data in the EPIC study : the Principal Component Partial R-square (PC-PR2) method
- 2014
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 10:6, s. 1074-1083
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Tidskriftsartikel (refereegranskat)abstract
- The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by H-1 NMR analysis on 807 serum samples originating from a nested liver cancer case-control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.
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| 4. |
- Lassale, Camille, et al.
(författare)
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Diet Quality Scores and Prediction of All-Cause, Cardiovascular and Cancer Mortality in a Pan-European Cohort Study
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Scores of overall diet quality have received increasing attention in relation to disease aetiology; however, their value in risk prediction has been little examined. The objective was to assess and compare the association and predictive performance of 10 diet quality scores on 10-year risk of all-cause, CVD and cancer mortality in 451,256 healthy participants to the European Prospective Investigation into Cancer and Nutrition, followed-up for a median of 12.8y. All dietary scores studied showed significant inverse associations with all outcomes. The range of HRs (95% CI) in the top vs. lowest quartile of dietary scores in a composite model including non-invasive factors (age, sex, smoking, body mass index, education, physical activity and study centre) was 0.75 (0.72-0.79) to 0.88 (0.84-0.92) for all-cause, 0.76 (0.69-0.83) to 0.84 (0.76-0.92) for CVD and 0.78 (0.73-0.83) to 0.91 (0.85-0.97) for cancer mortality. Models with dietary scores alone showed low discrimination, but composite models also including age, sex and other non-invasive factors showed good discrimination and calibration, which varied little between different diet scores examined. Mean C-statistic of full models was 0.73, 0.80 and 0.71 for all-cause, CVD and cancer mortality. Dietary scores have poor predictive performance for 10-year mortality risk when used in isolation but display good predictive ability in combination with other non-invasive common risk factors.
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| 5. |
- Li, Kuanrong, et al.
(författare)
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Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts
- 2018
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Ingår i: Breast Cancer Research. - : BioMed Central. - 1465-5411 .- 1465-542X. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction.METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
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| 6. |
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| 7. |
- Muller, David C., et al.
(författare)
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Modifiable causes of premature death in middle-age in Western Europe : Results from the EPIC cohort study
- 2016
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Ingår i: BMC Medicine. - : Springer Science and Business Media LLC. - 1741-7015. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Life expectancy is increasing in Europe, yet a substantial proportion of adults still die prematurely before the age of 70 years. We sought to estimate the joint and relative contributions of tobacco smoking, hypertension, obesity, physical inactivity, alcohol and poor diet towards risk of premature death. Methods: We analysed data from 264,906 European adults from the EPIC prospective cohort study, aged between 40 and 70 years at the time of recruitment. Flexible parametric survival models were used to model risk of death conditional on risk factors, and survival functions and attributable fractions (AF) for deaths prior to age 70 years were calculated based on the fitted models. Results: We identified 11,930 deaths which occurred before the age of 70. The AF for premature mortality for smoking was 31 % (95 % confidence interval (CI), 31-32 %) and 14 % (95 % CI, 12-16 %) for poor diet. Important contributions were also observed for overweight and obesity measured by waist-hip ratio (10 %; 95 % CI, 8-12 %) and high blood pressure (9 %; 95 % CI, 7-11 %). AFs for physical inactivity and excessive alcohol intake were 7 % and 4 %, respectively. Collectively, the AF for all six risk factors was 57 % (95 % CI, 55-59 %), being 35 % (95 % CI, 32-37 %) among never smokers and 74 % (95 % CI, 73-75 %) among current smokers. Conclusions: While smoking remains the predominant risk factor for premature death in Europe, poor diet, overweight and obesity, hypertension, physical inactivity, and excessive alcohol consumption also contribute substantially. Any attempt to minimise premature deaths will ultimately require all six factors to be addressed.
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| 8. |
- Agogo, George O., et al.
(författare)
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Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design : EPIC Case Study
- 2014
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:11, s. e113160-
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Tidskriftsartikel (refereegranskat)abstract
- In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted twopart regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.
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| 9. |
- Aleksandrova, Krasimira, et al.
(författare)
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Biomarker patterns of inflammatory and metabolic pathways are associated with risk of colorectal cancer : results from the European Prospective Investigation into Cancer and Nutrition (EPIC)
- 2014
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Ingår i: European Journal of Epidemiology. - : Springer Science and Business Media LLC. - 0393-2990 .- 1573-7284. ; 29:4, s. 261-275
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Tidskriftsartikel (refereegranskat)abstract
- A number of biomarkers of inflammatory and metabolic pathways are individually related to higher risk of colorectal cancer (CRC); however, the association between biomarker patterns and CRC incidence has not been previously evaluated. Our study investigates the association of biomarker patterns with CRC in a prospective nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC). During median follow-up time of 7.0 (3.7-9.4) years, 1,260 incident CRC cases occurred and were matched to 1,260 controls using risk-set sampling. Pre-diagnostic measurements of C-peptide, glycated hemoglobin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), reactive oxygen metabolites (ROM), insulin-like growth factor 1, adiponectin, leptin and soluble leptin receptor (sOB-R) were used to derive biomarker patterns from principal component analysis (PCA). The relation with CRC incidence was assessed using conditional logistic regression models. We identified four biomarker patterns 'HDL-C/Adiponectin fractions', 'ROM/CRP', 'TG/C-peptide' and 'leptin/sOB-R' to explain 60 % of the overall biomarker variance. In multivariable-adjusted logistic regression, the 'HDL-C/Adiponectin fractions', 'ROM/CRP' and 'leptin/sOB-R' patterns were associated with CRC risk [for the highest quartile vs the lowest, incidence rate ratio (IRR) = 0.69, 95 % CI 0.51-0.93, P-trend = 0.01; IRR = 1.70, 95 % CI 1.30-2.23, P-trend = 0.002; and IRR = 0.79, 95 % CI 0.58-1.07; P-trend = 0.05, respectively]. In contrast, the 'TG/C-peptide' pattern was not associated with CRC risk (IRR = 0.75, 95 % CI 0.56-1.00, P-trend = 0.24). After cases within the first 2 follow-up years were excluded, the 'ROM/CRP' pattern was no longer associated with CRC risk, suggesting potential influence of preclinical disease on these associations. By application of PCA, the study identified 'HDL-C/Adiponectin fractions', 'ROM/CRP' and 'leptin/sOB-R' as biomarker patterns representing potentially important pathways for CRC development.
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| 10. |
- Assi, Nada, et al.
(författare)
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Metabolic signature of healthy lifestyle and its relation with risk of hepatocellular carcinoma in a large European cohort
- 2018
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Ingår i: American Journal of Clinical Nutrition. - : American Society for Nutrition. - 0002-9165 .- 1938-3207. ; 108:1, s. 117-126
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Tidskriftsartikel (refereegranskat)abstract
- Background: Studies using metabolomic data have identified metabolites from several compound classes that are associated with disease-related lifestyle factors.Objective: In this study, we identified metabolic signatures reflecting lifestyle patterns and related them to the risk of hepatocellular carcinoma (HCC) in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.Design: Within a nested case-control study of 147 incident HCC cases and 147 matched controls, partial least squares (PLS) analysis related 7 modified healthy lifestyle index (HLI) variables (diet, BMI, physical activity, lifetime alcohol, smoking, diabetes, and hepatitis) to 132 targeted serum-measured metabolites and a liver function score. The association between the resulting PLS scores and HCC risk was examined in multivariable conditional logistic regression models, where ORs and 95% CIs were computed.Results: The lifestyle component's PLS score was negatively associated with lifetime alcohol, BMI, smoking, and diabetes, and positively associated with physical activity. Its metabolic counterpart was positively related to the metabolites sphingomyelin (SM) (OH) C14:1, C16:1, and C22:2, and negatively related to glutamate, hexoses, and the diacyl-phosphatidylcholine PC aaC32:1. The lifestyle and metabolomics components were inversely associated with HCC risk, with the ORs for a 1-SD increase in scores equal to 0.53 (95% CI: 0.38, 0.74) and 0.28 (0.18, 0.43), and the associated AUCs equal to 0.64 (0.57, 0.70) and 0.74 (0.69, 0.80), respectively.Conclusions: This study identified a metabolic signature reflecting a healthy lifestyle pattern which was inversely associated with HCC risk. The metabolic profile displayed a stronger association with HCC than did the modified HLI derived from questionnaire data. Measuring a specific panel of metabolites may identify strata of the population at higher risk for HCC and can add substantial discrimination compared with questionnaire data. This trial was registered at clinicaltrials.gov as NCT03356535.
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